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Tuesday, July 20, 2010

In my spare time, I'm preparing a compilation of essays on various estimates of our country's wrongful conviction rate. As I draft them, I'll publish them here. When I'm done with all of them, I'll compile them into a single document and make it available on Scribd for free, and on Amazon for a minimal cost.

The chapters will be numbered according to the predicted wrongful conviction rate, in percent. So far I have posted Chapter 0.027, Chapter 0.5, and Chapter 1.0. Keep in mind that we have a lot of people incarcerated, around 2.5 million. That means a wrongful conviction rate of only 1%, if applicable to all those incarcerated, means we have wrongfully imprisoned 25,000 people. A wrongful conviction rate of 10%, if applied to all those incarcerated, would mean we currently have a quarter million people incarcerated for crimes they did not commit.

I didn't pull the 10% number out of thin air. We'll soon be talking about estimates that exceed 10%. For now, it's time to look at the Poveda number.

Chapter 1.4
The Poveda Number

Tony Poveda is a man who understands the value of long division.

Recall from Chapter 1.0 that Poveda divided Rosenbaum’s compilation of 23 New York murder exonerations by a corresponding compilation of 2,276 murder convictions. Because he was careful to insure that both his numerator (23) and his denominator (2,276) were from consistent and meaningful populations, his quotient (.01010) came to be one of the first empirically-based wrongful conviction rates.

Poveda reported his work in “Estimating Wrongful Convictions,” an article published in the September 2001 issue of Justice Quarterly. That article actually provided three different estimates of the wrongful conviction rate. One was work with the Rosenbaum number. One will be discussed later when we talk about prisoner surveys. The third estimate I have named after him, and I will discuss it in the remainder of this brief chapter.

The Poveda number is actually quite similar in its construction to the Rosenbaum number. Instead of investigating a range of years, as had Rosenbaum, Poveda focused on a single year, 1995. Instead of focusing on all exonerations in New York, as had Rosenbaum, Poveda started with 24 court-ordered releases of prisoners convicted of murder in New York.

Poveda realized that some of those court-ordered releases were not to be counted as a wrongful convictions. Most of them involved prisoners who were re-tried and found guilty of lesser crimes. The wrongful conviction community does not typically count such cases as a wrongful conviction. Poveda needed to correct his number before he had a good numerator.

Poveda discovered another study, conducted by the New York State Department of Correctional Services, that determined only 21.2% of the court-ordered releases in murder cases were because the prisoner had been acquitted on retrial or had his case dismissed without a retrial. Assuming that percentage was valid for 1995 (though the NYSDS study was based on the years 1980 and 1987), Poveda determined his numerator to be:

24 x .212 = 5

Because he worked with only a single year, his numerator wasn’t all that large. I’ll allow Poveda to conclude the analysis in his own words.

However, the relevant population base for calculating prevalence of wrongful convictions [the denominator] is the number of murder commitments to the Department of Correctional Services. A study conducted by the New York State Defenders Association … found that the median time from conviction to reversal (acquittal/dismissal) in wrongful homicide convictions in New York is approximately three years. … Accordingly, murder inmates released by court-ordered discharge in 1995 were probably committed to the Department of Correctional Services in 1992, when 357 murder commitments were made. … If five of these were wrongful convictions, this translates to an error rate of 1.4 percent in murder convictions/commitments.

And there it is.

5 / 357 = 0.0140 = 1.4%

I have seen Poveda’s number cited frequently. However, it has less statistical significance than the Rosenbaum number of 1.0%, which is described in the very same paper as the Poveda number of 1.4%. I suspect it’s because people are in search of the most dramatic wrongful conviction rate.

As is the case with the Rosenbaum number, the Poveda number is probably low because it is based on convictions resolved in a time period before DNA testing and before a somewhat greater willingness to accept that people will confess to crimes they didn’t commit.

In the next chapter, we will look at an empirically based exoneration rate based at least in part on DNA exonerations.

Notes:
1. Tony Poveda is a professor at State University of New York Plattsburgh.

2. I suggest Poveda should not have calculated two separate wrongful conviction rates. Instead, I argue he should have summed the Rosenbaum and Poveda numerators, summed the Rosenbaum and Poveda denominators, and divided, as such:

(23 + 5) / (2,276 + 357) = 28 / 2633 = 0.0106 = 1.1%

The combined result is little different than the Rosenbaum number because the Rosenbaum number is based on a substantially larger sample. The Poveda number should not be relied upon because it lacks statistical significance. Neither number should be relied upon because each are based on a pre-DNA populations.